Fit Poolwise Regression Models (pooling)
Functions for calculating power and fitting regression models in studies where a biomarker is measured in ‘pooled’ samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.

Bayesian Graphical Estimation using Spike-and-Slab Priors (ssgraph)
For Bayesian inference in undirected graphical models using spike-and-slab priors, for multivariate continuous data. The package is implemented the recent improvements in the Bayesian graphical models literature, including Wang (2015) <doi:10.1214/14-BA916>.

R Interface to ‘Apache Tika’ (rtika)
Extract text or metadata from over a thousand file types, using Apache Tika <https://…/>. Get either plain text or structured XHTML content.

Dashboard with Semantic UI Support for ‘shiny’ (semantic.dashboard)
Basic functions for creating semantic UI dashboard. This package adds support for a powerful UI library semantic UI – <http://…/> to your dashboard and enables you to stay compatible with ‘shinydashboard’ functionalities.

Hyper-Ensemble Smote Undersampled Random Forests (hyperSMURF)
Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data.